1,721,031 research outputs found
The Observation of Interaction Between Pseudomonas aeruginosa and Cornea by the Use of Two-Photon Fluorescence and Second Harmonic Generation Microscopy
No full text眼睛是我們用來接收與探索浩瀚世界最直接的感官，而角膜則位於眼睛的最前線，因此也最容易受到外來異物的侵襲與破壞，造成角膜炎，嚴重會影響視力造成失明的傷害。在隱形眼鏡愈來愈普及的趨勢下，因隱形眼鏡配戴習慣不正確造成角膜感染的議題也逐漸受到重視，而綠膿桿菌正是其中最重要的一種感染病菌，其會附著在角膜上，然後伺機地破壞角膜組織，綠膿桿菌毒性相當猛烈，故其破壞的效率相當地快，角膜大概一至二天就會被摧殘潰爛，因此即時診斷出角膜感染的病原體是相當重要的。利用低破壞性的雙光子螢光及二倍頻非線性光學技術可以讓我們清楚地觀察角膜的表皮細胞及膠原蛋白纖維組織的型態及結構。我們選擇牛眼角膜做組織培養，然後再對各個樣品注射細菌溶液或生理緩衝液（PBS），對個別感染的角膜樣品注射的區域附近在不同的時間點取影像。隨著注射後的時間增加，角膜基質的膠原蛋白被破壞的程度也顯著的增加，細菌也有增殖的趨勢以及向周圍散落蔓延，並且在角膜內有大量的自發螢光物質產生。而在對照組中，角膜基質的膠原蛋白訊號沒有衰減的很多，並且沒有大量的自發螢光訊號產生。我們可以藉此初步的實驗做一些型態上的探討，至於定性定量的分析，以及定位的影像可能還要再做實驗方法的改良。我們期盼此多光子顯微術在日後可以應用在醫學臨床上，輔助醫師不需做破壞性的組織切片來直接診斷角膜感染的程度並加以即時治療。The eyes are the most direct sensory organ in our body to receive and explore the world, and the corneas residing in the outmost frontline easily result in suffering invasion and destruction by external matter, lead to keratitis, affect vision ability and lose vision seriously. In the generalization of contact lens develops day by day, on the issue associated with corneal infection due to incorrect habit of contact lens is put much emphasis. Pseudomonas aeruginosa is one of the most important infectious pathogens, it will adhere on the cornea and destroy the corneal tissue opportunistically with its quite violent virulence. The infected corneas will be attacked to necrosis by one to two days with very fast destructive speed. It is very important to diagnosis the infectious pathogen in time. We can investigate the morphology and the structure of the corneal epithelium and stroma collagen by the use of minimally invasive two-photon fluorescence and second harmonic generation microscopy. We culture the bovine cornea as our specimens, and inject into each specimen with bacteria suspension or PBS, and image the region nearby the injection hole of each cornea at different time. As the time went by, there are more increase in the severity of the stromal collagen destruction, the more outspreading pattern and proliferation occurred in bacteria activity, and more abundant substance with auto-fluorescence within the cornea. For our controls, there are a little decay of the signals of stromal collagen, and no emergence of the huge auto-fluorescence signals. We can make the morphological discuss via these preliminary experiment, and as for the quality, quantity analysis, and the fixed point observation should need the refinement of the experimental methods. We expect that this multi-photon microscopy could apply in clinic in the future, to assist doctors in diagnosing the degree of corneal infection and treating in time without destructive histology biopsy.口試委員會審定書…………………………………………………………………….i
誌謝……………………………………………………………………………………ii
中文摘要……………………………………………………………………………...iii
英文摘要……………………………………………………………...………………iv
第一章 前言…………………………………………………………………………1
第一節、研究動機…..……………………………………………………………1
第二節、文獻回顧…..……………………………………………………………2
第三節、實驗簡介…..……………………………………………………………3
第四節、未來展望…..……………………………………………………………3
第二章 實驗原理、儀器及材料……………………………………………………5
第一節、顯微術….…..……………………………………………………………5
第二節、非線性光學原理…..………………………………………………….10
第一小節、單光子的光學機制……………………………………….........10
一、單光子吸收螢光激發的機制理論……………………………….10
二、輻射的機制理論………………………………………………….14
第二小節、雙光子的光學機制…………………………………………….17
一、雙光子螢光激發機制的理論…………………………………….17
二、二倍頻機制的理論……………………………………………….19
第三節、儀器架設…..………………………………………………………...…25
第四節、實驗材料…….…………………………………………………………28
第一小節、角膜…………………………………………………………….28
第二小節、綠膿桿菌(Pseudomonas aeruginosa)………...……………….32
第三章、實驗步驟與方法…..……………………………………………………...…34
第一節、角膜的處理與培養….…..…………………………………………..…34
第二節、細菌溶液的調配與角膜內注射……………………………………….35
第三節、感染時間及影像的測試……………………………………………….39
第四章、實驗結果與討論………………………………………………………...…42
第五章、結論……………………………………………………………………...…50
參考資料…………………………………………………………………………...…5
The Political and Economic Analysis on India's Rising (1991-2008)
No full text本論文從宏觀巨視的角度，以印度1991年到2008年間的政治、經濟發展為分析重點，輔以觀察同時期間台灣與印度間的關係發展脈絡，希冀以印度崛起的政治與經濟分析，得以作為台灣與印度發展關係時的借鏡參考。
受到蘇聯解體、國內政治與經濟情勢，乃至於中國改革開放成果的啟發與影響，印度在1991年採行經濟的改革開放措施；十餘年的改革開放，對印度的經濟發展、國家建設及國計民生帶來巨大且受人矚目的成效，高盛（Goldman Sachs）公司在2003年將印度與巴西、中國、俄羅斯並列為廿一世紀最具發展潛力的「金磚四國」（BRIC），印度的經濟發展潛力成為全球矚目的焦點。
全文以「自獨立至九○年代的政治經濟脈動」、「九○年代起的政治經濟新發展」、「印度崛起的機會」、「印度崛起的挑戰」及「台灣與印度關係的政治經濟觀察」為架構，以實存的時空脈絡、政治與經濟面向，以及內部與外部因素等三個維度，對印度的政治與經濟演進加以觀察，對印度的政、經發展軌跡進行探究，對印度崛起的機會與挑戰進行內部及外部因素的評估，全文在完成前述觀察、探究與評估之後，以其所得結果，從政治與經濟觀點，對台灣與印度雙邊關係的演進及互動加以觀察。This thesis tries to use a macro vision to observe and analyze India’s political and economic development and the Taiwan-India relationship period from 1991 to 2008.
In the post-Cold War era, the collapse of the Soviet Union proved that the planned economy was a failure, coupled with India’s political and economic situation, also aroused by the achievements of China’s economic reform. India adapted economic reform measures in 1991.
In less than two decades, India’s economic reforms have already reached remarkable achievements in many aspects: in infrastructure, industrial and economic development and national living standards have made great progress.
In 2003, Goldman Sachs picked India as one of BRIC states, and predicted that India will be one of the most economically promising countries in 21st century. India’s economic and market potential is attracting global focus.
This thesis has a four-core analytical framework: “India’s political and economic development, from independence to 1990”, “Political and economic development after 1990’s”, “Opportunities for India’s rising”, “Challenges to India’s rising”, and “Observations on the Taiwan-India political and economic relationship”.
In accordance with the three dimensions of "chronological", "political and economic aspects", and "internal and external factors", this thesis will observe and analyze India’s political and economic development, using internal and external factors to assess the opportunities and challenges for India’s rising.
In the last part of this thesis, according to the above observations and findings, from a political and economic point of view, observing the evolution of relations between Taiwan and India
Motion adaptive de-interlacing by horizontal motion detection and enhanced ELA processing
No full textChi Yuan Zu Chao--Architectural Culture and Historical Context of Tainan San-Shan Kings Temple
No full text本文以臺南三山國王廟的視覺現象以及背後歷史文化脈絡為主題，首先針對臺南三山國王廟現存的建築風格的視覺外觀、風格特色，做出清晰的分析與描述。進一步關心這樣的藝術現象是如何被形塑，可以透過文獻與物質資料的比較和分析，來歸納臺南三山國王廟在清代的興修中，建築物是如何被信眾創建、興修，以致於形成今日的規模與狀態。年出土地基發現，在清代早期臺南山國王廟與韓文公祠顯然是兩間獨立的廟宇。現今以三山國王廟中心的建築型態、藝術表現，顯然存在著一個歷史與建築的變遷過程，與其信仰原鄉的廣東地區有著密不可分的關係。故，本文也嘗試透過田野的實地調查，描繪了廣東地區不同方言群所呈現出來的不同建築風格。將臺南三山國王廟置入這樣的光譜中，強烈顯示出其中具有濃厚的潮汕建築傳統的成分。種「如潮」的祈願，不只表現在信仰對象的挑選上，也表現在其建築文化上。該廟獨特的風格特徵，追溯起來，也隱然藏身這種「如潮」的願望中，體現在建築文化表現的物質層面。粵人移民傳統社會中處於領導地位的文人與官員，較傾向於具有儒家色彩的信仰。非士大夫階層的群眾們，則崇敬故鄉的福神三山國王。後來隨著臺南府學地位的衰弱，潮州商人階層可能在光緒年間取得了廟宇的主導權，也導致三山國王廟吞併韓文公祠的結果。天后聖母則可能因為某種程度上扮演了與其他族群交流的角色，進而與韓文公祠分庭抗禮。於是在今日所遺存的臺南三山國王廟建築中，便可以看到三祠並立卻獨尊三山國王的視覺現象。藉著建築出潮州風格的建築，祭拜著潮州原鄉的神祉，當時的移民們不但表現出對故鄉的文化記憶，也是面對仍然不穩定的移民社會中，許許多多競爭挑戰的反應。緒論 - 1 -、問題的提出 - 1 -、研究史回顧 - 3 -一）三山國王信仰的研究 - 3 -二）傳統建築之研究 - 10 -、研究目的與方向 - 13 -一章 雙廟合一---現存臺南三山國王廟建築的風格特徵與其形成 - 17 -一節 三祠並列與主次有別---臺南三山國王廟的建築風格特徵 - 17 -、三祠並列的平面佈局 - 17 -、主次有別的視覺語言 - 18 -、木作的特徵 - 20 -二節 雙廟分立到三祠並列---臺南三山國王廟與韓文公祠的興修與分合 - 22 -、臺南三山國王廟的創建問題與臺灣粵籍移民的活動 - 23 -、三山國王廟與韓文公祠雙廟分立的形成 - 29 -，以及這種演變產生的時間點。 - 36 -三節 臺南三山國王廟現存建築斷代蠡測 - 36 -結 - 39 -二章 建築視覺形象的臺灣脈絡分析 - 41 -一節 臺南三山國王廟與臺灣祠廟建築 - 41 -、與臺灣現存三山國王廟傳統建築之比較 - 42 -、與臺灣其他傳統祠廟建築之比較 - 43 -二節 匠師所遺留的訊息 - 52 -、臺南三山國王廟墨跡所遺留的藝匠術語 - 53 -結 - 57 -三章 追尋原鄉的建築意象 - 58 -一節 廣東傳統建築的共通性與差異性 - 59 -、空間佈局 - 59 -、視覺外觀 - 61 -、大木結構 - 66 -二節 潮州建築樑架承重與裝飾的多元樣貌 - 68 -結 - 73 -四章 臺南三山國王廟的歷史社會脈絡 - 75 -一節 清代臺灣的廣東族群 - 75 -二節 光緒以前的臺南三山國王廟 - 80 -、碑文與匾額中所見的贊助者現象 - 81 -、府城的聯境組織與臺南三山國王廟 - 87 -三節 光緒年間與日治時期臺南府城內的廣東族群 - 90 -、光緒年間府城嶺南建築的修建 - 90 -、日治時期社會變遷的衝擊 - 95 -結 - 109 -論 - 111 -築、信仰與人群 - 111 -論 - 114 -考書目 - 116
Algorithm and Architecture Analysis of the Video Signal Conversion for 2D and 3D Video
No full text人類對於視覺效果的追求永不停歇。從黑白電視、彩色電視，直至今日的數位電視，是人類對視覺界限的挑戰，也是科技精益求精的表現。在液晶電視及電漿電視如此蓬勃發展的現在，舊有的交錯式掃瞄電視訊號如何還原為漸進式掃瞄以輸出至此類電視上，畫質和速度對於使用者的影響甚巨。而在液晶電視之外，目前各大廠亦皆已開發出能夠呈現三維立體視覺感的液晶螢幕，也因此預言著下一代的顯示介面將以三維立體視覺為主。在如此的潮流下，立體視覺(Stereo 3D Video)內容的提供及壓縮也將會成為新時代電視的必須。傳統取得立體影像方法，皆需靠額外設備提供資料才得以將這第三維度轉換成深度。但其實有大量的影像資料，在過去早已拍攝成為平面影像供平面電視觀看，這些資料在未來勢必也會大量出現，若搭配立體顯示器卻無法發揮顯示器效果，將會是一種無形的浪費。綜觀人眼視覺的概念，即便不靠雙眼，不靠水晶體多處對焦亦能夠對畫面構築出立體視覺。人眼深度機制包含雙眼視覺、圖像理解及圖像認知層面皆能幫助「看」出物體的深度，也因此IMAX實體電影都需透過專家來進行平面影像至立體影像的轉換。若能參透其中道理並移植至電子產品上，那麼在立體影像的擷取端就可以下更少功夫，直接在電子產品裡將所有的平面影像內容都轉換為立體影像。
為了追求極致的畫面品質，本論文針對此多種不同的世代交替，在舊世代的影像訊號上轉換至新世代的規格時，影像訊號的重建與還原做信號處理。在二維影像方面，本論文提出將交錯式掃瞄影像轉換至非交錯掃瞄的可適應性動像補償去交錯方法突破傳統以動態適應性去交錯方法的限制，大幅拉高重建後影像的解析度。而在三維影像方面，本論文不僅是對於如何擷取三維影像做了總匯的整理，更提出一套二維影像至三維影像即時轉換系統，我們所要提出的二維至三維影像轉換系統，即是透過人腦在立體視覺的概念：包含有雙眼視覺、圖像理解及圖像認知等層面，將這些概念實體化為演算法並且進行硬體設計。使得家庭多媒體平台得以嵌入這套系統，可直接將平面影像轉換為立體影像，使得立體影像生成不再要從擷取端就做起，更使得過去龐大的影像資料在未來有更上一層樓的能力。
此一系統俱備有幾個部份：三種深度重建工具，深度圖融合，以及可調適性的深度影像內差。三種深度重建工具將不同的人類深度線索轉換為演算法使用，深度圖融合控制不同的重建工具，在不同的場景和內容對不同的重建工具進行融合；深度影像內差器再進行深度影像繪製，畫出左右兩眼的影像，以方便輸出至立體顯示器播放。此數種不同的轉換工具，都需要極大量的以點為基礎的運算，在這方面使用硬體加速有其必要性。最後的系統，是一可進行即時的平面影像至立體影像的轉換器。此轉換器可供家庭多媒體平台廠商嵌入其系統，增加出將所有電視影集、運動節目及電影都轉換成立體的功能，充分和立體顯示器結合，發揮其最大效用。Human are pursuing the reality of vision devices. The video devices improve
from monochrome television to 3D-LCD today. The video signals also vary in all of
these devices. In this dissertation, the video signal conversion for 2D and 3D video
are discussed in two different parts: de-interlacing and 2D-to-3D conversion. The deinterlacing
methods recover the lost data in temporal and spatial domain of a 2D video
sequence. The 2D-to-3D conversion produces the whole dimensional data as the depth
map of a 2D video, then it converts the depth map and 2D video into 3D video.
The transition between interlaced scanned TV signals and progressive scanned
TV signals hindered the quality improvement of the new display panels. Post-processing
such as de-interlacing has become a great index for a TV decoder showing its performance.
In Part I, three kinds of de-interlacing methods are described first: the intrafield
de-interlacing, the motion adaptive de-interlacing, and the motion compensated deinterlacing.
Second, for better de-interlaced image quality, we proposed an intra-field deinterlacing
algorithm named “Extended Intelligent Edge-based Line Average” (EIELA).
Its VLSI module implementation is also stated. Third, for near-perfect de-interlaced image
quality, a de-interlacing algorithm using adaptive global and local motion estimation/-
compensation is proposed. It consists of the global and local motion estimation/compensation,
4-field motion adaptation, the block-based directional edge interpolation, and the
GMC/MC/MA block mode decision module. All defects such as jagged effects, blurring,
line-crawling, and feathering are suppressed lower than the traditional methods. Moreover,
the true motion information is extracted accurately by the 4-field motion estimation
and global motion information.
In Part II, we first make a detailed survey for different kinds of 3D video capturing
methods. There are three kinds of 3D video capturing methods: the active sensor
based methods, the passive sensor based methods, and the 2D-to-3D conversion. After
analyzing the previous works, a real-time automatic depth fusion 2D-to-3D conversion
system is proposed for the home multimedia platform.
In Part III, we tried to convert the binocular, monocular, and pictorial depth cue
to depth reconstruction algorithms. Five novel algorithms and hardware architecture are
presented. The depth reconstruction algorithms can be classified into three categories:
the motion parallax based depth reconstruction which utilizes the binocular depth cue, the
image based depth reconstruction which uses the monocular depth cue, and the consciousness
based depth reconstruction which map the perspective in pictorial depth cue to depth
gradient. After the depth reconstruction, a priority depth fusion algorithm is proposed to
integrate all the depth maps. Then a multiview depth image based rendering method is
presented to provide multiview image rendering technique for the multiview 3D-LCD.
One-dimensional cross search dense disparity estimation is proposed for the motion
parallax based depth reconstruction. The fast algorithm utilizes the characteristics of
the motion parallax and trinocular cameras. As the motion parallax is induced by camera
motion, 1D cross search tends to find better and more smooth results for a true depth map.
A symmetric trinocular property for trinocular camera stereo matching is also described.
Then a 2D full search dense disparity estimation hardware architecture design is designed
for the real-time operation of the motion parallax based depth reconstruction. The dense
disparity estimation needs to calculate the disparity vectors of each depth pixel. With
the features of resolution switching and high specification, the proposed hardware architecture
uses a data assignment unit as a small buffer to achieve a IP-based design. The
hardware can be switched to three different depth pixel resolution in real-time.
Depth from Focus and short-term motion assisted color segmentation are proposed
for the image based depth reconstruction. The DfF method adapts the “blurriness”
characteristic while taking pictures with large aperture camera. After extracting the object
from the blurring areas, the depth of the object is set to the focus distance of the taken
picture. The second image-based depth reconstruction method is the depth map generation
by short-term motion assisted color segmentation. It achieves a smooth depth map
generation both in the spatial and temporal domain. But both methods would face the
moving cameras problem and the tuning of various different type image sequences in the
future. They should be combined with the depth from geometry perspective and other
depth cues to produce more accurate depth map.
For the consciousness based depth reconstruction, we have presented a fundamental
detection algorithm based on the structural components analysis with robustness.
It is suitable for images with distinct object edges. The proposed method for vanishing
line and vanishing point detection provides direct analysis from image structure without
complicated math calculation. The proposed method is feasible for a particular image sequence
without prior temporal information, and guarantees that dominant vanishing lines
are detected correctly with high probability and accuracy. The proposed block-based algorithm
which still holds the regular block data flow feature is much faster, simpler and
efficient. As for the 2D-to-3D conversion procedure, the proposed vanishing line and
point detection gives great help for the overall scene knowledge, and the conversion proceeds
more easily.
After retrieving all the depth maps from different depth cues, we proposed a priority
depth fusion method to integrate the three depth maps. It considers the priority of
the depth maps in six aspects: the scene adaptability, the temporal consistency, perceptibility,
correctness, fineness, and cover area. In order to obtain a comfortable depth map, the six aspects should be deliberated to decide the priority. We also proposed a per-pixel
texture mapping depth image based rendering algorithm which can be accelerated by the
GPU. The proposed algorithm converts points to vertices. Then an image plane represents
the original frame and depth map is constructed. Through the GPU pipeline, the left and
right image can be rendered out. Even for a free viewpoint application, as long as the
GPU draws more than 49.6Mtriangles per second, the multiview DIBR still can run at
real-time. And the proposed DIBR also speed up the previous design for 38 times. After
having these algorithms, an automatic depth fusing 2D-to-3D conversion system is described.
The proposed system generates the depth map of most of the commercial video
with hardware acceleration. With the calculation of GPU, the depth map and the original
2D image are converted to stereo images for showing on the 3D display devices. Huge
amount of the 2D contents such as DVD or TV programs are able to convert and show on
the 3D display devices on the enduser side.
In summary, this dissertation presents an intra-field de-interlacing hardware architecture,
named extended intelligent edge based line average and an adaptive local/-
global motion compensated de-interlacing method for the de-interlacing of 2D video. For
2D video signals to 3D video signals, an automatic depth fusing 2D-to-3D conversion
system is proposed to utilize the human depth cues to convert 2D video to 3D video.
There are five algorithms proposed in this 2D-to-3D conversion system using different
depth cues: one dimensional cross search dense disparity estimation, depth map generation
with short-term motion assisted color segmentation, block-based vanishing line/point
detection, per-pixel multiview depth image based rendering, and priority depth fusion.
There is also a hardware architecture of 2D full search dense disparity estimation implemented
to combine with the whole 2D-to-3D conversion system. The proposed 2D-to-3D
conversion not only produces acceptable depth map for 2D video but also renders multiview
video from the depth information and the 2D video.Contents vii
List of Figures xxii
List of Tables 1
1 Introduction 3
1.1 Trends in TV Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.1 NTSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.2 HDTV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.3 3D-TV: ATTEST . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Trends in Video Display Device . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 3D Video Display . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 13
I 2D Video Signal Conversion 15
2 Survey of TV Signal De-interlacing 17
2.1 Aliasing Effect in the sampling of TV . . . . . . . . . . . . . . . . . . . 18
2.2 Conventional De-interlacing Algorithms . . . . . . . . . . . . . . . . . . 20
2.2.1 Intra-field De-interlacing . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Motion Adaptive De-interlacing . . . . . . . . . . . . . . . . . . 22
2.2.3 Motion Compensated De-interlacing . . . . . . . . . . . . . . . . 22
2.2.4 GMC De-interlacing . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.5 Prior Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 The Extended Intelligent Edge-based Line Average Algorithm and Architecture
29
3.1 Extended Intelligent Edge-based Line Average Algorithm . . . . . . . . . 29
3.2 Extended Intelligent Edge-based Line Average - EIELA . . . . . . . . . . 32
3.3 EIELA VLSI Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 Hardware Simulation . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.2 PSNR Comparison and New Test Sequence . . . . . . . . . . . . 38
3.5 Subjective View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 Adaptive 4-Field Global/Local motion compensated De-interlacing 43
4.1 Proposed Adaptive 4-Field Global/Local motion compensated De-interlacing 44
4.1.1 Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.2 Global Motion Estimation/Compensation . . . . . . . . . . . . . 47
4.1.3 Same Parity 4-Field Local Motion Estimation . . . . . . . . . . . 51
4.2 Experimental Results and Performance Analysis . . . . . . . . . . . . . . 62
4.2.1 Analysis of Instruction Counts and Hardware Cost . . . . . . . . 62
4.2.2 Objective Performance Comparison . . . . . . . . . . . . . . . . 64
4.2.3 Comparisons of Subjective View . . . . . . . . . . . . . . . . . . 67
4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
II Survey of 3D Video Capturing 77
5 Survey of 3D Capturing Apparatus 79
5.1 The Principle of Stereo Vision . . . . . . . . . . . . . . . . . . . . . . . 80
5.2 The History of Stereo Vision . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3 Depth Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.4 3D Video Capturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.4.1 Active sensor based Method . . . . . . . . . . . . . . . . . . . . 88
5.4.2 Passive sensor based Method . . . . . . . . . . . . . . . . . . . . 89
5.4.3 Signal Processing based Method . . . . . . . . . . . . . . . . . . 93
6 Review of 2D-to-3D Conversion 95
6.1 Depth Cues in Human Vision . . . . . . . . . . . . . . . . . . . . . . . . 96
6.2 Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.3 Depth Image based Rendering . . . . . . . . . . . . . . . . . . . . . . . 98
6.4 Depth from focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.5 Image-based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5.1 Still Image Analysis Method . . . . . . . . . . . . . . . . . . . . 101
6.5.2 Geometry Perspective Method . . . . . . . . . . . . . . . . . . . 102
6.6 Motion-based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.6.1 Depth from motion . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.6.2 Structure from motion . . . . . . . . . . . . . . . . . . . . . . . 104
6.7 Depth Fusion Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.8 The Comparison Between the 3D Video Capturing Methods . . . . . . . 105
6.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
III 2D-to-3D Conversion 109
7 Motion Parallax based Depth Reconstruction 111
7.1 Prior Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.2 Disparity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.3 One-Dimensional Fast Cross Search Dense Disparity Estimation Algorithm116
7.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.3.2 Proposed One-Dimensional Fast Cross Search Dense Disparity
Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
7.4 Two-Dimensional Full Search Dense Disparity Estimation Hardware Architecture
Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4.2 Dense Disparity Estimation Architecture . . . . . . . . . . . . . 124
7.5 Experimental Results and Architecture Performance . . . . . . . . . . . . 127
7.5.1 One-Dimensional Fast Cross Search Dense Disparity Estimation
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 127
7.5.2 Performance of the Two-Dimensional Full Search Dense Disparity
Estimation Hardware Architecture Design . . . . . . . . . . . 129
7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8 Image-based Depth Reconstruction 131
8.1 Proposed Object-based Depth from focus algorithm . . . . . . . . . . . . 132
8.1.1 Focus measure and Depth Estimation . . . . . . . . . . . . . . . 133
8.1.2 Step Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
8.1.3 Depth Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.1.4 Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.1.5 Re-interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8.1.6 Clean Background . . . . . . . . . . . . . . . . . . . . . . . . . 137
8.1.7 Efficient Depth Interpolation with Color Segmentation and Watershed
Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 137
8.2 Proposed Depth Map Generation for 2D-to-3D Conversion by Short-Term
Motion Assisted Color Segmentation . . . . . . . . . . . . . . . . . . . . 138
8.2.1 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
8.3.1 Object-based Depth From Focus . . . . . . . . . . . . . . . . . . 145
8.3.2 Short-Term Motion Assisted Color Segmentation Experimental
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
8.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
9 Consciousness-based Depth Reconstruction 155
9.1 Depth from Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
9.2 Proposed Block-based Vanishing Line and Point Detection Algorithm . . 157
9.2.1 Edge detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
9.2.2 Energy filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
9.2.3 Block object finding . . . . . . . . . . . . . . . . . . . . . . . . 161
9.2.4 Object combination and selection . . . . . . . . . . . . . . . . . 162
9.2.5 Dominant vanishing lines acquisition . . . . . . . . . . . . . . . 164
9.2.6 Vanishing point detection . . . . . . . . . . . . . . . . . . . . . . 165
9.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
10 Depth Image Based Rendering 169
10.1 Prior Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
10.1.1 Image shifting DIBR . . . . . . . . . . . . . . . . . . . . . . . . 171
10.2 Proposed Texture Mapping Multi-view DIBR . . . . . . . . . . . . . . . 172
10.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
10.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
11 Automatic Depth Fusing 2D-to-3D Conversion System 181
11.1 Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
11.2 Proposed Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
11.3 Motion Parallax based Depth Reconstruction module . . . . . . . . . . . 184
11.4 Image based Depth Reconstruction module . . . . . . . . . . . . . . . . 185
11.5 Consciousness based Depth Reconstruction module . . . . . . . . . . . . 185
11.6 Priority Depth Fusion module . . . . . . . . . . . . . . . . . . . . . . . 186
11.7 Multiview DIBR module . . . . . . . . . . . . . . . . . . . . . . . . . . 186
11.8 System Hardware/Software Co-Design . . . . . . . . . . . . . . . . . . . 188
11.9 Design Methodology and Flow . . . . . . . . . . . . . . . . . . . . . . . 188
11.10Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
11.11Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
12 Conclusions 199
12.1 Principal Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
12.1.1 Adaptive 4-Field Global/Local motion compensated De-interlacing 200
12.1.2 Automatic Depth Fusing 2D-to-3D Conversion System . . . . . . 201
12.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
12.2.1 Multi-Sensor 3D Video Capturing . . . . . . . . . . . . . . . . . 204
12.2.2 Post-processing for 3D-LCD devices . . . . . . . . . . . . . . . 204
12.2.3 Intelligent Vehicles and Robots . . . . . . . . . . . . . . . . . . 205
Bibliography 207
Curriculum Vitae 219
Publication List 22
Application of Back Propagation Artificial Neural Network to Real Time Analysis and Prediction of the Total Suspended Solids in Northern Taiwan Reservoirs
No full text水庫集水區的治理、開發與操作，常會遭遇地表土壤沖蝕所產生的非點源污染。為了能夠有效防止此類災害的發生，隨時監測集水區的整治情況，以及建立完備的懸浮固體濃度即時監測系統是必要的。本研究以中華民國行政院環境保護署新山水庫、翡翠水庫、石門水庫、寶山水庫、永和山水庫、明德水庫水質監測數據查詢資料庫中1993-2005年間的資料來進行分析。從資料庫中所選許的水質參數有比導電度、溶氧、酸鹼值、濁度、溫度、採樣月份、葉綠素α、總磷、總硬度及透明度。然後利用水質之間的群集分析、測站之間的顯著性分析、水庫之間的相關性分析，進一步選取合適的水質參數和測站。再利用類神經網路架構來進行訓練、驗證網路即時推估懸浮固體濃度。經過一系列分析及觀察，發現類神經網路可以由水質參數推估懸浮固體濃度，但其推估的準確度依地理位置及土壤分布的不同而有所改變。結果亦顯示以類神經網路模式在一些條件下可利用數種容易量測的水質資料來推估不易量測的懸浮固體濃度。此外，以石門水庫水質資料利用倒傳遞類神經網路來預測懸浮固體濃度並作驗證，其結果顯示，預測與實測值的迴歸式係數達到0.90，表示推估趨勢十分良好；且網路輸出與期望輸出的判別係數R2達到0.63，顯示以本研究所提出之方法和石門水庫各項水質參數應用在其懸浮固體濃度之推估上，可預測到各個峰值，且可相當準確的預估其變化趨勢。In the management of reservoir, non-point source pollutions caused by surface soil erosion are frequently encountered. In order to prevent this kind of problems, it is necessary to continually monitor the watershed of the reservoir as well as to real-time monitor the total suspended solid(TSS). The data of the water quality of Xin-Shan reservoir, Feitsui reservoir, Shimen reservoir, Baoshan reservoir, Yonghe-Shan reservoir, and Mingd reservoir used in the study were provided by Environmental Protection Administration of the Executive Yuan, R.O.C.. These data included electrical conductivity, dissolved oxygen, pH value, turbidity, temperature, month, chlorophyll-α, total phosphorus, total hardness, and transmissivity, in the period from 1993 to 2005. Suitable water quality parameters and observation stations were further chosen from the statistical results by cluster analysis of the water quality, dominance analysis of the observation stations, and correlation coefficient of the reservoirs. Back propagation artificial neural network was applied to real time analysis and prediction of the total suspended solids. However the estimation accuracy would vary with locations and soil types. From the results, it was also found that the nural network model may be used to estimate the concentration of suspended solids, which is difficult to be real time measured, by using several parameters of water quality, which are easier to be measured, under some specific conditions. When back propagation network was modified to predict the real time total suspended solids in Shimen reservoir, the results showed that the predicted variation tendency of total suspended solids in network output agrees well with that in expected output, the R2 can reach 0.63, the regression coefficient can reach 0.90. It could be concluded that the method of back propagation artificial neural network and water quality can be used to rapidly and accurately estimate TSS.誌 謝 I
摘 要 II
ABSTRACT III
圖目錄 VII
表目錄 IX
第一章 前言 - 1 -
1.1 研究動機 - 1 -
1.2 研究目的 - 2 -
第二章 文獻回顧 - 4 -
2.1 懸浮固體濃度 - 4 -
2.2 懸浮固體濃度量測方法 - 4 -
2.3 統計分析 - 6 -
2.4 類神經網路推估 - 6 -
第三章 研究方法 - 9 -
3.1 研究區域簡介 - 11 -
3.1.1翡翠水庫環境背景資料 - 13 -
3.1.2新山水庫環境背景資料 - 13 -
3.1.3石門水庫環境背景資料 - 14 -
3.1.4寶山水庫環境背景資料 - 14 -
3.1.5永和山水庫環境背景資料 - 15 -
3.1.6明德水庫環境背景資料 - 15 -
3.2 參數資料概述 - 15 -
3.3 相關性 - 17 -
3.4 綜合變方分析 - 17 -
3.5 群集分析 - 18 -
3.6 倒傳遞類神經網路 - 19 -
第四章 倒傳遞類神經網路建置 - 22 -
4.1 輸入參數選取 - 22 -
4.1.1 測站選則 - 22 -
4.1.2 水質參數 - 24 -
4.2 隱藏神經元配置及比較 - 29 -
4.3 倒傳遞類神經網路即時推估懸浮固體濃度建置 - 31 -
4.4 倒傳遞類神經網路動態即時預測懸浮固體濃度建置 - 32 -
4.5 小結 - 34 -
第五章 網路模式之訓練與驗證 - 36 -
5.1 倒傳遞類神經網路即時推估懸浮固體濃度與驗證 - 36 -
5.2 倒傳遞類神經網路預測懸浮固體濃度與驗證 - 40 -
5.3 小結 - 43 -
第六章 結論與建議 - 45 -
6.1 結論 - 45 -
6.2 建議 - 46 -
參考文獻 - 48 -
附錄 - 52 -
附錄A:水庫水質站資料綜合分析 - 53 -
附錄B:水庫環境背景資料補充 - 59 -
附錄C:水庫水質測站位置 - 64 -
作者簡歷 - 70
Real-Time Depth Image based Rendering Hardware Accelerator for Advanced Three Dimensional Television System
No full textDepth map generation for 2D-to-3D conversion by short-term motion assisted color segmentation
No full text
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